Ramble: Masking with Equalize — OZONE Asylum, home of the Mad Scientists

Ramble: Masking with Equalize

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Your Text: Insert Slimies » Insert UBB Code » Close Last Tag \| All Tags UBB Help	I'm not sure about here on the Asylum, but I do have a great deal of my stuff archived. Goes back like to almost when I came here. Been here 6 years, so my personal archives go back around 5 or so. I really got to get to getting my junk on wiki. Not just rambles, but more plug-ins, parlour tricks, and interesting observations. Oh my dear, sweat wiki... why have I forsaken thee? How about a quick chat about Equalize? Sounds good to me. I'm sure most of you folks are familiar with Auto Levels. Sorely loved, yet delightfully dreaded. Auto Levels will find a high point and a low point using histogram, and then stretch the data based on those points. When you set the clipping percent in Auto Levels, you are dipping into the histogram. In recent versions, Auto Levels comes with various options for what histogram data the ladle dips into. To see how Auto Levels stretches the data, just use Histogram. Maybe even try different methods of histogram dipping. It should be fairly easy to see how linear the stretching is. What about Equalize? It stretches the data as well, but in what manner? Again, do before-and-after of Histogram to see. In using the Histogram palette, you may have to watch the cache level. Depending on cache level, you can get very different histograms for the exact same image. Can't say I know the rules for when the Histogram palette decides to up the cache level. Just keep an eye on the upper-right for the little warning triangle. Eventually, we get something like this: [img]http://tech-slop.serveit.org/wiki/images/histogramequalize.gif[/img] In the Manual Levels example, pretty easy to see how the gaps are mostly evenly distributed (I suspect B-Spline distribution of gaps). But how are the gaps distributed in the Equalize example? The mountain of values got stretched, but how did Equalize know? If you can believe it, Equalize is about probability and manipulating it. The histogram is built, sorted, and then used as a look-up table. That's it in a nutshell. (I had a link explaining this rather well, but it appears to have gone bye bye.) The result is that you should end up with pretty evenly distributed values. Unfortunately, this method is more of an approximation. In order for this to work with absolute precision, you have to start with an image that is absolutely perfect. Well, perfect in the data, and I have never seen that happen and I don't think I ever will. What is the probability of finding an image with perfect data? Heh. We end up with an approximation and gaps where they 'should' be. Is there a use for this fugly operation known as Equalize? There is at least one very functional technique that I've discovered, and it gets into midtones. Snap! Did I just say the [i]M[/i] word? I think I did. If you use Equalize to extract HML, you are doing so based on probability and end up working with percents based on the image as a whole. If you use Equalize to get M in HML, you should end up with approximately 50% of an image. If you get H in HML, you should end up with approximately 25% of an image. And L being approx. the last 25%. This can be an excessively useful parlour trick. When my eyeballs can't handle getting the midtones that I want, I'll use Equalize to do it's best for me. Mostly in images that are too washed out, or when the contrast is way too high. Are there any drawbacks of using Equalize? Of course, but I'll leave this to you. Consider this a part of knowing when to use Equalize and when not to use Equalize. [small](Edited by [url=http://www.ozoneasylum.com/user/351]warjournal[/url] on 10-10-2006 00:34)[/small] Loading...
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